System and method for optimizing utility pipe sensors placement using artificial intelligence technology

Abstract

A computer-implemented method and system for determining placement of a sensor component on a utility pipe. Data relating to the utility pipe is inputted which is processed to generate one or more variables. One or more models are trained, via the one or more variables, to produce an output indicative of a likelihood of failure variable associated with the utility pipe from each model. The outputs from all models are preferably combined into an ensemble output indicative of a likelihood of failure associated with the utility pipe. A consequence of failure variable associated with the utility pipe is determined preferably utilizing a plurality of weighted variables. A sensor placement determinative variable is then determined contingent upon the ensemble output and the consequence of failure variable associated with the utility pipe. Feedback data is then provided indicative of physical placement of one or more sensor components associated with the utility pipe based at least in part on the sensor placement determinative variable.

What is claimed is: 
     
         1 . A computer-implemented method using machine learning techniques for determining placement of a sensor component on a utility pipe, comprising:
 processing, in the at least computer device, inputted utility pipe data to generate one or more variables;   training, in the at least computer device, one or more machine learning models upon the one or more variables, to produce an output indicative of a likelihood of failure (LoF) variable associated with the utility pipe from each model; and   calculating, in the at least computer device, a Consequence of Failure (CoF) variable associated with the utility pipe utilizing one or more variables related to the consequences of failure of a certain pipe;   calculating, in the one or more computer devices, utilizing the LoF and CoF variables, a sensor placement determinative variable; and   providing output data, from the one or more computer devices, indicative of physical placement locations of one or more sensor components associated with the utility pipe based at least in part on the sensor placement determinative variable.   
     
     
         2 . The computer-implemented method of  claim 1 , further including combining, in the at least computer device, the outputs from each of the machine learning models into an ensemble output indicative of the LoF variable associated with the utility pipe wherein the sensor placement determinative variable is calculated utilizing the ensemble output and the CoF variable. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein calculating the LoF and CoF variables utilize a plurality of weighted variables. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein the inputted data includes information accessed from at least a utility company associated with the utility pipe. 
     
     
         5 . The computer-implemented method of  claim 4 , wherein the inputted data includes unstructured data whereby the at least computer device utilizes a Large Language Model (LLM) to normalize the unstructured data into structured data suitable for the use in the one or more machine learning models, wherein the unstructured data may include text data, image data, video data, acoustic data, or other multi-modality signal sensor data. 
     
     
         6 . The computer-implemented method of  claim 5 , wherein the at least computer device utilizing the LLM identifies, and corrects, omissions and errors present in the inputted data. 
     
     
         7 . The computer-implemented method of  claim 5 , further including, ranking, by the at least computer device utilizing the LLM, the physical placement locations of the one or more sensor components wherein the LLM provides a user explanation for the ranking order of the one or more sensor components. 
     
     
         8 . The computer-implemented method of  claim 5 , further including, providing real-time monitoring of the one or more sensor components, by the at least computer device utilizing the LLM, for generating alert notifications of one or more detected changing conditions inflicted upon the one or more sensor components. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein processing the inputted data further includes generating data transformations for use in training of the one or more machine learning models or executing the calculation of the LoF variable. 
     
     
         10 . The computer-implemented method of  claim 1 , wherein the utility pipe is one of a water pipe or sewer pipe and the one or more sensor components consists of one more of a pressure sensor, flow sensor or acoustic sensor. 
     
     
         11 . A computer system for determining placement of a sensor component on a utility pipe, comprising:
 a memory configured to store instructions;   a processor disposed in communication with said memory, wherein said processor upon execution of the instructions is configured to:   process inputted utility pipe data to generate one or more variables;   train one or more machine learning models upon the one or more variables, to produce an output indicative of a likelihood of failure (LoF) variable associated with the utility pipe from each model; and   calculate a Consequence of Failure (CoF) variable associated with the utility pipe utilizing one or more variables related to the consequences of failure of a certain pipe;   calculate, utilizing the LoF and CoF variables, a sensor placement determinative variable; and   provide output data indicative of physical placement locations of one or more sensor components associated with the utility pipe based at least in part on the sensor placement determinative variable.   
     
     
         12 . The computer system of  claim 1 , wherein the processor further combines the outputs from each of the machine learning models into an ensemble output indicative of the LoF variable associated with the utility pipe wherein the sensor placement determinative variable is calculated utilizing the ensemble output and the CoF variable. 
     
     
         13 . The computer system of  claim 11 , wherein calculating the LoF and CoF variables utilize a plurality of weighted variables. 
     
     
         14 . The computer system of  claim 11 , wherein the inputted data includes information accessed from at least a utility company associated with the utility pipe. 
     
     
         15 . The computer system of  claim 14 , wherein the inputted data includes unstructured data whereby the at least computer device utilizes a Large Language Model (LLM) to normalize the unstructured data into structured data suitable for the use in the one or more machine learning models, wherein the unstructured data may include text data, image data, video data, acoustic data, or other multi-modality signal sensor data. 
     
     
         16 . The computer system of  claim 15 , wherein the processor utilizing the LLM identifies, and corrects, omissions and errors present in the inputted data. 
     
     
         17 . The computer system of  claim 15 , wherein the processor utilizing the LLM, provides a ranking order for the physical placement locations of the one or more sensor components wherein the LLM provides a user explanation for the ranking order of the one or more sensor components. 
     
     
         18 . The computer system of  claim 15 , wherein the processor utilizing the LLM, provides real-time monitoring of the one or more sensor components for generating alert notifications of one or more detected changing conditions inflicted upon the one or more sensor components. 
     
     
         19 . The computer system of  claim 1 , wherein processing the inputted data further includes generating data transformations for use in training of the one or more machine learning models or executing the calculation of the LoF variable. 
     
     
         20 . The computer system of  claim 10 , wherein the utility pipe is one of a water pipe or sewer pipe and the one or more sensor components consists of one more of a pressure sensor, flow sensor or acoustic sensor.